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Meine LE, Müller-Bardorff M, Recher D, Paersch C, Schulz A, Spiller T, Galatzer-Levy I, Kowatsch T, Fisher AJ, Kleim B. Network analyses of ecological momentary emotion and avoidance assessments before and after cognitive behavioral therapy for anxiety disorders. J Anxiety Disord 2024; 106:102914. [PMID: 39153405 DOI: 10.1016/j.janxdis.2024.102914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 06/26/2024] [Accepted: 08/12/2024] [Indexed: 08/19/2024]
Abstract
Negative emotions and associated avoidance behaviors are core symptoms of anxiety. Current treatments aim to resolve dysfunctional coupling between them. However, precise interactions between emotions and avoidance in patients' everyday lives and changes from pre- to post-treatment remain unclear. We analyzed data from a randomized controlled trial where patients with anxiety disorders underwent 16 sessions of cognitive behavioral therapy (CBT). Fifty-six patients (68 % female, age: M = 33.31, SD = 12.45) completed ecological momentary assessments five times a day on 14 consecutive days before and after treatment, rating negative emotions and avoidance behaviors experienced within the past 30 min. We computed multilevel vector autoregressive models to investigate contemporaneous and time-lagged associations between anxiety, depression, anger, and avoidance behaviors within patients, separately at pre- and post-treatment. We examined pre-post changes in network density and avoidance centrality, and related these metrics to changes in symptom severity. Network density significantly decreased from pre- to post-treatment, indicating that after therapy, mutual interactions between negative emotions and avoidance were attenuated. Specifically, contemporaneous associations between anxiety and avoidance observed before CBT were no longer significant at post-treatment. Effects of negative emotions on avoidance assessed at a later time point (avoidance instrength) decreased, but not significantly. Reduction in avoidance instrength positively correlated with reduction in depressive symptom severity, meaning that as patients improved, they were less likely to avoid situations after experiencing negative emotions. Our results elucidate mechanisms of successful CBT observed in patients' daily lives and may help improve and personalize CBT to increase its effectiveness.
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Affiliation(s)
- Laura E Meine
- Experimental Psychopathology and Psychotherapy, Department of Psychology, University of Zurich, Zurich, Switzerland; Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland.
| | - Miriam Müller-Bardorff
- Experimental Psychopathology and Psychotherapy, Department of Psychology, University of Zurich, Zurich, Switzerland; Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland
| | - Dominique Recher
- Experimental Psychopathology and Psychotherapy, Department of Psychology, University of Zurich, Zurich, Switzerland; Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland
| | - Christina Paersch
- Experimental Psychopathology and Psychotherapy, Department of Psychology, University of Zurich, Zurich, Switzerland; Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland; University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Ava Schulz
- Experimental Psychopathology and Psychotherapy, Department of Psychology, University of Zurich, Zurich, Switzerland; Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland
| | - Tobias Spiller
- Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland; Department of Consultation-Liaison Psychiatry and Psychosomatic Medicine, University Hospital Zurich, Zurich, Switzerland
| | - Isaac Galatzer-Levy
- Psychiatry, New York University School of Medicine, New York, NY, United States
| | - Tobias Kowatsch
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland; School of Medicine, University of St. Gallen, St. Gallen, Switzerland; Centre for Digital Health Interventions, Department of Management, Technology and Economics, ETH Zurich, Zurich, Switzerland
| | - Aaron J Fisher
- Department of Psychology, University of California at Berkeley, Berkeley, CA, United States
| | - Birgit Kleim
- Experimental Psychopathology and Psychotherapy, Department of Psychology, University of Zurich, Zurich, Switzerland; Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland.
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Fernández-Álvarez J, Colombo D, Gómez Penedo JM, Pierantonelli M, Baños RM, Botella C. Studies of Social Anxiety Using Ambulatory Assessment: Systematic Review. JMIR Ment Health 2024; 11:e46593. [PMID: 38574359 PMCID: PMC11027061 DOI: 10.2196/46593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 01/28/2024] [Accepted: 02/07/2024] [Indexed: 04/06/2024] Open
Abstract
BACKGROUND There has been an increased interest in understanding social anxiety (SA) and SA disorder (SAD) antecedents and consequences as they occur in real time, resulting in a proliferation of studies using ambulatory assessment (AA). Despite the exponential growth of research in this area, these studies have not been synthesized yet. OBJECTIVE This review aimed to identify and describe the latest advances in the understanding of SA and SAD through the use of AA. METHODS Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a systematic literature search was conducted in Scopus, PubMed, and Web of Science. RESULTS A total of 70 articles met the inclusion criteria. The qualitative synthesis of these studies showed that AA permitted the exploration of the emotional, cognitive, and behavioral dynamics associated with the experience of SA and SAD. In line with the available models of SA and SAD, emotion regulation, perseverative cognition, cognitive factors, substance use, and interactional patterns were the principal topics of the included studies. In addition, the incorporation of AA to study psychological interventions, multimodal assessment using sensors and biosensors, and transcultural differences were some of the identified emerging topics. CONCLUSIONS AA constitutes a very powerful methodology to grasp SA from a complementary perspective to laboratory experiments and usual self-report measures, shedding light on the cognitive, emotional, and behavioral antecedents and consequences of SA and the development and maintenance of SAD as a mental disorder.
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Affiliation(s)
- Javier Fernández-Álvarez
- Department of Basic and Clinical Psychology and Psychobiology, Jaume I University, Castellon de la Plana, Spain
- Fundación Aiglé, Buenos Aires, Argentina
| | - Desirée Colombo
- Department of Basic and Clinical Psychology and Psychobiology, Jaume I University, Castellon de la Plana, Spain
| | | | | | - Rosa María Baños
- Polibienestar Research Institute, University of Valencia, Valencia, Spain
- Department of Personality, Evaluation, and Psychological Treatments, University of Valencia, Valencia, Spain
- Ciber Fisiopatologia Obesidad y Nutricion (CB06/03 Instituto Salud Carlos III), Madrid, Spain
| | - Cristina Botella
- Department of Basic and Clinical Psychology and Psychobiology, Jaume I University, Castellon de la Plana, Spain
- Ciber Fisiopatologia Obesidad y Nutricion (CB06/03 Instituto Salud Carlos III), Madrid, Spain
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Schmitter-Edgecombe M, Luna C, Dai S, Cook DJ. Predicting daily cognition and lifestyle behaviors for older adults using smart home data and ecological momentary assessment. Clin Neuropsychol 2024:1-25. [PMID: 38503715 DOI: 10.1080/13854046.2024.2330143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 03/07/2024] [Indexed: 03/21/2024]
Abstract
OBJECTIVE Extraction of digital markers from passive sensors placed in homes is a promising method for understanding real-world behaviors. In this study, machine learning (ML) and multilevel modeling (MLM) are used to examine types of digital markers and whether smart home sensors can predict cognitive functioning, lifestyle behaviors, and contextual factors measured through ecological momentary assessment (EMA). METHOD Smart home sensors were installed in the homes of 44 community-dwelling midlife and older adults for 3-4 months. Sensor data were categorized into eight digital markers. Participants responded to iPad-delivered EMA prompts 4×/day for 2 wk. Prompts included an n-back task and survey on recent (past 2 h) lifestyle and contextual factors. RESULTS ML marker rankings revealed that sensor counts (indicating increased activity) and time outside the home were among the most influential markers for all survey questions. Additionally, MLM revealed for every 1000 sensor counts, mental sharpness, social, physical, and cognitive EMA responses increased by 0.134-0.155 points on a 5-point scale. For every additional 30-minutes spent outside home, social, physical, and cognitive EMA responses increased by 0.596, 0.472, and 0.157 points. Advanced ML joint classification/regression significantly predicted EMA responses from smart home digital markers with error of 0.370 on a 5-point scale, and n-back performance with a normalized error of 0.040. CONCLUSION Results from ML and MLM were complimentary and comparable, suggesting that machine learning may be used to develop generalized models to predict everyday cognition and track lifestyle behaviors and contextual factors that impact health outcomes using smart home sensor data.
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Affiliation(s)
| | - Catherine Luna
- Department of Psychology, Washington State University, Pullman, WA, USA
| | - Shenghai Dai
- College of Education, Washington State University, Pullman, WA, USA
| | - Diane J Cook
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, USA
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Eberhardt ST, Schaffrath J, Moggia D, Schwartz B, Jaehde M, Rubel JA, Baur T, André E, Lutz W. Decoding emotions: Exploring the validity of sentiment analysis in psychotherapy. Psychother Res 2024:1-16. [PMID: 38415369 DOI: 10.1080/10503307.2024.2322522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 02/15/2024] [Indexed: 02/29/2024] Open
Abstract
OBJECTIVE Given the importance of emotions in psychotherapy, valid measures are essential for research and practice. As emotions are expressed at different levels, multimodal measurements are needed for a nuanced assessment. Natural Language Processing (NLP) could augment the measurement of emotions. The study explores the validity of sentiment analysis in psychotherapy transcripts. METHOD We used a transformer-based NLP algorithm to analyze sentiments in 85 transcripts from 35 patients. Construct and criterion validity were evaluated using self- and therapist reports and process and outcome measures via correlational, multitrait-multimethod, and multilevel analyses. RESULTS The results provide indications in support of the sentiments' validity. For example, sentiments were significantly related to self- and therapist reports of emotions in the same session. Sentiments correlated significantly with in-session processes (e.g., coping experiences), and an increase in positive sentiments throughout therapy predicted better outcomes after treatment termination. DISCUSSION Sentiment analysis could serve as a valid approach to assessing the emotional tone of psychotherapy sessions and may contribute to the multimodal measurement of emotions. Future research could combine sentiment analysis with automatic emotion recognition in facial expressions and vocal cues via the Nonverbal Behavior Analyzer (NOVA). Limitations (e.g., exploratory study with numerous tests) and opportunities are discussed.
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Moggia D, Bennemann B, Schwartz B, Hehlmann MI, Driver CC, Lutz W. Process-Based psychotherapy personalization: considering causality with continuous-time dynamic modeling. Psychother Res 2023; 33:1076-1095. [PMID: 37306112 DOI: 10.1080/10503307.2023.2222892] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 06/02/2023] [Indexed: 06/13/2023] Open
Abstract
Psychotherapy can be improved by integrating the study of mediators (how it works) and moderators (for whom it works). To demonstrate this integration, we studied the relationship between resource activation, problem-coping experiences and symptoms in cognitive-behavior therapy (CBT) for depression, to obtain preliminary insights on causal inference (which process leads to symptom improvement?) and prediction (which one for whom?). A sample of 715 patients with depression who received CBT was analyzed. Hierarchical Bayesian continuous time dynamic modeling was used to study the temporal dynamics between the variables analyzed within the first ten sessions. Depression and self-efficacy at baseline were examined as predictors of these dynamics. There were significant cross-effects between the processes studied. Under typical assumptions, resource activation had a significant effect on symptom improvement. Problem-coping experience had a significant effect on resource activation. Depression and self-efficacy moderated these effects. However, when system noise was considered, these effects may be affected by other processes. Resource activation was strongly associated with symptom improvement. To the extent of inferring causality, for patients with mild-moderate depression and high self-efficacy, promoting resource activation can be recommended. For patients with severe depression and low self-efficacy, promoting problem-coping experiences can be recommended.
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Schwartz B, Uhl J, Atzil-Slonim D. Editorial: Assessments and measures in psychotherapy research: going beyond self-report data. Front Psychiatry 2023; 14:1276222. [PMID: 37711417 PMCID: PMC10497970 DOI: 10.3389/fpsyt.2023.1276222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 08/21/2023] [Indexed: 09/16/2023] Open
Affiliation(s)
- Brian Schwartz
- Department of Psychology, University of Trier, Trier, Germany
| | - Jessica Uhl
- Department of Psychology, University of Trier, Trier, Germany
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8
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Terhürne P, Schwartz B, Baur T, Schiller D, Eberhardt ST, André E, Lutz W. Validation and application of the Non-Verbal Behavior Analyzer: An automated tool to assess non-verbal emotional expressions in psychotherapy. Front Psychiatry 2022; 13:1026015. [PMID: 36386975 PMCID: PMC9650367 DOI: 10.3389/fpsyt.2022.1026015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 10/12/2022] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Emotions play a key role in psychotherapy. However, a problem with examining emotional states via self-report questionnaires is that the assessment usually takes place after the actual emotion has been experienced which might lead to biases and continuous human ratings are time and cost intensive. Using the AI-based software package Non-Verbal Behavior Analyzer (NOVA), video-based emotion recognition of arousal and valence can be applied in naturalistic psychotherapeutic settings. In this study, four emotion recognition models (ERM) each based on specific feature sets (facial: OpenFace, OpenFace-Aureg; body: OpenPose-Activation, OpenPose-Energy) were developed and compared in their ability to predict arousal and valence scores correlated to PANAS emotion scores and processes of change (interpersonal experience, coping experience, affective experience) as well as symptoms (depression and anxiety in HSCL-11). MATERIALS AND METHODS A total of 183 patient therapy videos were divided into a training sample (55 patients), a test sample (50 patients), and a holdout sample (78 patients). The best ERM was selected for further analyses. Then, ERM based arousal and valence scores were correlated with patient and therapist estimates of emotions and processes of change. Furthermore, using regression models arousal and valence were examined as predictors of symptom severity in depression and anxiety. RESULTS The ERM based on OpenFace produced the best agreement to the human coder rating. Arousal and valence correlated significantly with therapists' ratings of sadness, shame, anxiety, and relaxation, but not with the patient ratings of their own emotions. Furthermore, a significant negative correlation indicates that negative valence was associated with higher affective experience. Negative valence was found to significantly predict higher anxiety but not depression scores. CONCLUSION This study shows that emotion recognition with NOVA can be used to generate ERMs associated with patient emotions, affective experiences and symptoms. Nevertheless, limitations were obvious. It seems necessary to improve the ERMs using larger databases of sessions and the validity of ERMs needs to be further investigated in different samples and different applications. Furthermore, future research should take ERMs to identify emotional synchrony between patient and therapists into account.
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Affiliation(s)
- Patrick Terhürne
- Clinical Psychology and Psychotherapy, University of Trier, Trier, Germany
| | - Brian Schwartz
- Clinical Psychology and Psychotherapy, University of Trier, Trier, Germany
| | - Tobias Baur
- Chair for Human Centered Artificial Intelligence, Augsburg University, Augsburg, Germany
| | - Dominik Schiller
- Chair for Human Centered Artificial Intelligence, Augsburg University, Augsburg, Germany
| | | | - Elisabeth André
- Chair for Human Centered Artificial Intelligence, Augsburg University, Augsburg, Germany
| | - Wolfgang Lutz
- Clinical Psychology and Psychotherapy, University of Trier, Trier, Germany
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Abstract
Outcome measurement in the field of psychotherapy has developed considerably in the last decade. This review discusses key issues related to outcome measurement, modeling, and implementation of data-informed and measurement-based psychological therapy. First, an overview is provided, covering the rationale of outcome measurement by acknowledging some of the limitations of clinical judgment. Second, different models of outcome measurement are discussed, including pre-post, session-by-session, and higher-resolution intensive outcome assessments. Third, important concepts related to modeling patterns of change are addressed, including early response, dose-response, and nonlinear change. Furthermore, rational and empirical decision tools are discussed as the foundation for measurement-based therapy. Fourth, examples of clinical applications are presented, which show great promise to support the personalization of therapy and to prevent treatment failure. Finally, we build on continuous outcome measurement as the basis for a broader understanding of clinical concepts and data-driven clinical practice in the future. Expected final online publication date for the Annual Review of Clinical Psychology, Volume 18 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Wolfgang Lutz
- Department of Psychology, University of Trier, Trier, Germany;
| | - Brian Schwartz
- Department of Psychology, University of Trier, Trier, Germany;
| | - Jaime Delgadillo
- Clinical and Applied Psychology Unit, Department of Psychology, University of Sheffield, Sheffield, United Kingdom
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Vogel C, Schobel J, Schlee W, Engelke M, Pryss R. UNITI Mobile-EMI-Apps for a Large-Scale European Study on Tinnitus. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2358-2362. [PMID: 34891756 DOI: 10.1109/embc46164.2021.9630482] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
More and more observational studies exploit the achievements of mobile technology to ease the overall implementation procedure. Many strategies like digital phenotyping, ecological momentary assessments or mobile crowdsensing are used in this context. Recently, an increasing number of intervention studies makes use of mobile technology as well. For the chronic disorder tinnitus, only few long-running intervention studies exist, which use mobile technology in a larger setting. Tinnitus is characterized by its heterogeneous patient's symptom profiles, which complicates the development of general treatments. In the UNITI project, researchers from different European countries try to unify existing treatments and interventions to cope with this heterogeneity. One study arm (UNITI Mobile) exploits mobile technology to investigate newly implemented interventions types, especially within the pan-European setting. The goals are to learn more about the validity and usefulness of mobile technology in this context. Furthermore, differences among the countries shall be investigated. Practically, two native intervention apps have been developed for UNITI and the mobile study arm, which pose features not presented so far in other apps of the authors. Along the implementation procedure, it is discussed whether these features might leverage similar types of studies in future. Since instruments like the mHealth evidence reporting and assessment checklist (mERA), developed by the WHO mHealth technical evidence review group, indicate that aspects shown for UNITI Mobile are important in the context of health interventions using mobile phones, our findings may be of a more general interest and are therefore being discussed in the work at hand.
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Neethirajan S, Kemp B. Digital Phenotyping in Livestock Farming. Animals (Basel) 2021; 11:2009. [PMID: 34359137 PMCID: PMC8300347 DOI: 10.3390/ani11072009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 06/22/2021] [Accepted: 06/29/2021] [Indexed: 12/18/2022] Open
Abstract
Currently, large volumes of data are being collected on farms using multimodal sensor technologies. These sensors measure the activity, housing conditions, feed intake, and health of farm animals. With traditional methods, the data from farm animals and their environment can be collected intermittently. However, with the advancement of wearable and non-invasive sensing tools, these measurements can be made in real-time for continuous quantitation relating to clinical biomarkers, resilience indicators, and behavioral predictors. The digital phenotyping of humans has drawn enormous attention recently due to its medical significance, but much research is still needed for the digital phenotyping of farm animals. Implications from human studies show great promise for the application of digital phenotyping technology in modern livestock farming, but these technologies must be directly applied to animals to understand their true capacities. Due to species-specific traits, certain technologies required to assess phenotypes need to be tailored efficiently and accurately. Such devices allow for the collection of information that can better inform farmers on aspects of animal welfare and production that need improvement. By explicitly addressing farm animals' individual physiological and mental (affective states) needs, sensor-based digital phenotyping has the potential to serve as an effective intervention platform. Future research is warranted for the design and development of digital phenotyping technology platforms that create shared data standards, metrics, and repositories.
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Affiliation(s)
- Suresh Neethirajan
- Adaptation Physiology Group, Department of Animal Sciences, Wageningen University & Research, 6700 AH Wageningen, The Netherlands;
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12
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Beck AK, Kelly PJ, Deane FP, Baker AL, Hides L, Manning V, Shakeshaft A, Neale J, Kelly JF, Gray RM, Argent A, McGlaughlin R, Chao R, Martini M. Developing a mHealth Routine Outcome Monitoring and Feedback App ("SMART Track") to Support Self-Management of Addictive Behaviours. Front Psychiatry 2021; 12:677637. [PMID: 34220583 PMCID: PMC8249767 DOI: 10.3389/fpsyt.2021.677637] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 05/03/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Routine outcome monitoring (ROM) has been implemented across a range of addiction treatment services, settings and organisations. Mutual support groups are a notable exception. Innovative solutions are needed. SMART Track is a purpose built smartphone app designed to capture ROM data and provide tailored feedback to adults attending Australian SMART Recovery groups for addictive behaviour(s). Objective: Details regarding the formative stage of app development is essential, but often neglected. Improved consideration of the end-user is vital for curtailing app attrition and enhancing engagement. This paper provides a pragmatic example of how principles embedded in published frameworks can be operationalised to address these priorities during the design and development of the SMART Track app. Methods: Three published frameworks for creating digital health technologies ("Person-Based Approach," "BIT" Model and IDEAS framework) were integrated and applied across two stages of research to inform the development, design and content of SMART Track. These frameworks were chosen to ensure that SMART Track was informed by the needs and preferences of the end-user ("Person-Based"); best practise recommendations for mHealth development ("BIT" Model) and a collaborative, iterative development process between the multi-disciplinary research team, app developers and end-users (IDEAS framework). Results: Stage one of the research process generated in-depth knowledge to inform app development, including a comprehensive set of aims (clinical, research/organisation, and usage); clear articulation of the target behaviour (self-monitoring of recovery related behaviours and experiences); relevant theory (self-determination and social control); appropriate behavioural strategies (e.g., behaviour change taxonomy and process motivators) and key factors that may influence engagement (e.g., transparency, relevance and trust). These findings were synthesised into guiding principles that were applied during stage two in an iterative approach to app design, content and development. Conclusions: This paper contributes new knowledge on important person-centred and theoretical considerations that underpin a novel ROM and feedback app for people with addictive behaviour(s). Although person-centred design and best-practise recommendations were employed, further research is needed to determine whether this leads to improved usage outcomes. Clinical Trial Registration: Pilot Trial: http://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377336.
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Affiliation(s)
- Alison K. Beck
- Faculty of the Arts, Social Sciences and Humanities, School of Psychology, University of Wollongong, Wollongong, NSW, Australia
| | - Peter J. Kelly
- Faculty of the Arts, Social Sciences and Humanities, School of Psychology, University of Wollongong, Wollongong, NSW, Australia
- Illawarra Health and Medical Research Institute, University of Wollongong, Wollongong, NSW, Australia
| | - Frank P. Deane
- Faculty of the Arts, Social Sciences and Humanities, School of Psychology, University of Wollongong, Wollongong, NSW, Australia
- Illawarra Health and Medical Research Institute, University of Wollongong, Wollongong, NSW, Australia
| | - Amanda L. Baker
- School of Medicine and Public Health, University of Newcastle, Callaghan, NSW, Australia
| | - Leanne Hides
- Centre for Youth Substance Abuse Research, Lives Lived Well Group, School of Psychology, University of Queensland, St Lucia, QLD, Australia
| | - Victoria Manning
- Eastern Health Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Anthony Shakeshaft
- National Drug and Alcohol Research Centre, University of New South Wales, Randwick, NSW, Australia
| | - Joanne Neale
- Addictions Department, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - John F. Kelly
- Harvard Medical School, Harvard University, Boston, MA, United States
| | - Rebecca M. Gray
- Centre for Social Research in Health, Faculty of Arts and Social Sciences, UNSW Sydney, Sydney, NSW, Australia
| | | | | | - Ryan Chao
- GHO, Customer Experience Agency, Sydney, NSW, Australia
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